Chemical engineers conducting screening studies have access to a huge number of possible tests. These range from calculating basic features of the candidate materials, which can be done in factions of a second, to detailed real life experiments, which might takes weeks or months to prepare. Screening studies typically work by applying a cheaper test to all of the candidates and then selecting some top fraction of performers to move onto a second round and so on. Utilizing high performance computing, this approach allows for high-throughput in silico screening of huge databases of materials [1]}. For example the authors of [2]} apply three rounds of increasingly accurate but expensive computer simulated experiments to find the most promising candidate materials in a database of 137,953 structures. This method is also widely used in screens where all of the test are real life experiments, including in industry [3]}. In practise this approach has been shown to work well in many applications but the choice of which tests to apply and how many candidates to pass through each round tends to be carried out in an ad hoc fashion without the use of available statistical and AI techniques.
